When should I use DataHub instead of Marquez?

Enterprise data catalog with lineage, discovery, and governance in a single, scalable platform. Large organizations needing hundreds of pre-built ingestion connectors across all major data sources. Teams wanting a metadata platform that scales from a startup's first catalog to enterprise-wide governance

When should I use Marquez instead of DataHub?

Open-source data lineage collection using the OpenLineage standard across multiple tools. Teams wanting vendor-neutral lineage that integrates with Airflow, Spark, dbt, and Flink. Lightweight lineage tracking without the full overhead of a complete data catalog

What are the main weaknesses of DataHub?

Complex to self-host at production scale — requires Kafka, Elasticsearch, and MySQL at minimum. DataHub Cloud is the managed path; self-hosting requires significant DevOps investment. Feature breadth means initial configuration and onboarding can be overwhelming

What are the main weaknesses of Marquez?

Lineage-only — no data discovery, quality monitoring, or governance features beyond lineage. Requires instrumenting pipelines to emit OpenLineage events for each tool separately. Smaller community than DataHub or Amundsen for full data catalog and governance use cases

DataHub vs Marquez: Key Differences for Python Data Engineering

Data Governance & Metadata

DataHub

Modern Metadata Platform

★ 4.6

Apache-2.0

pip install acryl-datahub

Marquez

Metadata Service for Data Lineage

★ 4.3

Apache-2.0

pip install marquez-client

Side-by-Side Comparison

DataHub

Marquez

DataHub

Marquez

Best For

✓Enterprise data catalog with lineage, discovery, and governance in a single, scalable platform
✓Large organizations needing hundreds of pre-built ingestion connectors across all major data sources
✓Teams wanting a metadata platform that scales from a startup's first catalog to enterprise-wide governance

✓Open-source data lineage collection using the OpenLineage standard across multiple tools
✓Teams wanting vendor-neutral lineage that integrates with Airflow, Spark, dbt, and Flink
✓Lightweight lineage tracking without the full overhead of a complete data catalog

Best For

✓Enterprise data catalog with lineage, discovery, and governance in a single, scalable platform
✓Large organizations needing hundreds of pre-built ingestion connectors across all major data sources
✓Teams wanting a metadata platform that scales from a startup's first catalog to enterprise-wide governance

✓Open-source data lineage collection using the OpenLineage standard across multiple tools
✓Teams wanting vendor-neutral lineage that integrates with Airflow, Spark, dbt, and Flink
✓Lightweight lineage tracking without the full overhead of a complete data catalog

Weaknesses

•Complex to self-host at production scale — requires Kafka, Elasticsearch, and MySQL at minimum
•DataHub Cloud is the managed path; self-hosting requires significant DevOps investment
•Feature breadth means initial configuration and onboarding can be overwhelming

•Lineage-only — no data discovery, quality monitoring, or governance features beyond lineage
•Requires instrumenting pipelines to emit OpenLineage events for each tool separately
•Smaller community than DataHub or Amundsen for full data catalog and governance use cases

Weaknesses

•Complex to self-host at production scale — requires Kafka, Elasticsearch, and MySQL at minimum
•DataHub Cloud is the managed path; self-hosting requires significant DevOps investment
•Feature breadth means initial configuration and onboarding can be overwhelming

•Lineage-only — no data discovery, quality monitoring, or governance features beyond lineage
•Requires instrumenting pipelines to emit OpenLineage events for each tool separately
•Smaller community than DataHub or Amundsen for full data catalog and governance use cases

License

Apache-2.0

License

Apache-2.0

Install

pip install acryl-datahub

pip install marquez-client

Install

pip install acryl-datahub

pip install marquez-client

Rating

★ 4.6

★ 4.3

Rating

★ 4.6

★ 4.3

Key Features

DataHub

1Extensible metadata platform with a graph-based metadata model
2Automated ingestion connectors for 50+ sources via Python recipes
3Column-level lineage tracking across transformations and queries
4Data contracts for defining and enforcing schema and freshness expectations
5Browser-based search, governance workflows, and ownership management

Marquez

1OpenLineage-compliant metadata service for tracking dataset inputs and outputs
2Namespace and job model links pipeline runs to their data lineage
3REST API for emitting and querying lineage events
4Integrations with Airflow, Spark, dbt, and Great Expectations
5Visual lineage graph in the Marquez UI for impact analysis

How Python Data Engineers Use These Tools

DataHub

Python data engineers use DataHub's Python SDK and ingestion framework to crawl metadata from databases, dbt projects, and Airflow — writing YAML recipe files that the `datahub` CLI ingests on a schedule. Custom Python emitters push metadata about internal pipeline assets that built-in connectors don't cover.

Marquez

Python data engineers integrate Marquez with Airflow using the `openlineage-airflow` package, which automatically emits lineage events for each task — capturing which datasets a task reads and writes without any code changes. Engineers query the Marquez API to build impact analysis tools that identify downstream jobs affected by an upstream schema change.

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Individual Tool Pages

View DataHub details →View Marquez details →

Side-by-Side Comparison

DataHub

Marquez

DataHub

Marquez

Best For

✓Enterprise data catalog with lineage, discovery, and governance in a single, scalable platform
✓Large organizations needing hundreds of pre-built ingestion connectors across all major data sources
✓Teams wanting a metadata platform that scales from a startup's first catalog to enterprise-wide governance

✓Open-source data lineage collection using the OpenLineage standard across multiple tools
✓Teams wanting vendor-neutral lineage that integrates with Airflow, Spark, dbt, and Flink
✓Lightweight lineage tracking without the full overhead of a complete data catalog

Best For

✓Enterprise data catalog with lineage, discovery, and governance in a single, scalable platform
✓Large organizations needing hundreds of pre-built ingestion connectors across all major data sources
✓Teams wanting a metadata platform that scales from a startup's first catalog to enterprise-wide governance

✓Open-source data lineage collection using the OpenLineage standard across multiple tools
✓Teams wanting vendor-neutral lineage that integrates with Airflow, Spark, dbt, and Flink
✓Lightweight lineage tracking without the full overhead of a complete data catalog

Weaknesses

•Complex to self-host at production scale — requires Kafka, Elasticsearch, and MySQL at minimum
•DataHub Cloud is the managed path; self-hosting requires significant DevOps investment
•Feature breadth means initial configuration and onboarding can be overwhelming

•Lineage-only — no data discovery, quality monitoring, or governance features beyond lineage
•Requires instrumenting pipelines to emit OpenLineage events for each tool separately
•Smaller community than DataHub or Amundsen for full data catalog and governance use cases

Weaknesses

•Complex to self-host at production scale — requires Kafka, Elasticsearch, and MySQL at minimum
•DataHub Cloud is the managed path; self-hosting requires significant DevOps investment
•Feature breadth means initial configuration and onboarding can be overwhelming

•Lineage-only — no data discovery, quality monitoring, or governance features beyond lineage
•Requires instrumenting pipelines to emit OpenLineage events for each tool separately
•Smaller community than DataHub or Amundsen for full data catalog and governance use cases

License

Apache-2.0

License

Apache-2.0

Install

pip install acryl-datahub

pip install marquez-client

Install

pip install acryl-datahub

pip install marquez-client

Rating

★ 4.6

★ 4.3

Rating

★ 4.6

★ 4.3

Key Features

DataHub

1Extensible metadata platform with a graph-based metadata model
2Automated ingestion connectors for 50+ sources via Python recipes
3Column-level lineage tracking across transformations and queries
4Data contracts for defining and enforcing schema and freshness expectations
5Browser-based search, governance workflows, and ownership management

Marquez

1OpenLineage-compliant metadata service for tracking dataset inputs and outputs
2Namespace and job model links pipeline runs to their data lineage
3REST API for emitting and querying lineage events
4Integrations with Airflow, Spark, dbt, and Great Expectations
5Visual lineage graph in the Marquez UI for impact analysis

How Python Data Engineers Use These Tools